Nonlinear Dynamic Fault Detection Method Based on Isometric Mapping

Abstract

Abstract: The data collected from chemical process are strongly nonlinear and dynamic related. To solve this problem, a nonlinear dynamic fault detection method using dynamic isometric mapping (DISOMAP) manifold learning was proposed. It first extracts submanifold feature from original data set with adaptive neighbor parameters, which preserves geometric structure. Then linear regression projection mapping which maps the original high dimension space to a low dimension embedding space is used. Finally, T2 and SPE statistics are constructed in the process monitoring application. The simulation results of Tennessee Eastman process show that DISOMAP-based method is more effective than KPCA (kernel principal component analysis) for process monitoring and fault detection.

Key words: dynamic isometric mapping (DISOMAP), manifold learning, non-linear, fault detection

CLC Number:

TP277

ZHANG Ni, TIAN Xue-Min. Nonlinear Dynamic Fault Detection Method Based on Isometric Mapping[J]. Journal of Shanghai Jiaotong University, 2011, 45(08): 1202-1206.

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